A large branch of ordinal methods such as ordered logistic regression [1]} is based on the concept of latent cut-off points [2]}, [3]}, [4]}, [5]}. [6]} present an ordinal latent variable model that relies on a smooth bijective ordering transform of priors. Their model of conflict intensity incorporates the Goldstein scale, however not a temporal dimension. [7]} build a Hidden Markov Model with a transition matrix over latent, ordered states representing customer relationship dynamics. They constrain the matrix to adjacent state moves and learn transition probabilities with an ordered logit model. The ordinal characteristics behind the OMD are conceptually different as we tweak a stick-breaking process by repeatedly sorting Beta-sampled vectors.
